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Social LEAP Estimates Animal Poses (SLEAP)
==========================================
.. image:: https://sleap.ai/docs/_static/sleap_movie.gif
:width: 600px
**SLEAP** is an open source deep-learning based framework for multi-animal pose tracking `(Pereira et al., Nature Methods, 2022) <https://www.nature.com/articles/s41592-022-01426-1>`__. It can be used to track any type or number of animals and includes an advanced labeling/training GUI for active learning and proofreading.
Features
--------
* Easy, one-line installation with support for all OSes
* Purpose-built GUI and human-in-the-loop workflow for rapidly labeling large datasets
* Single- and multi-animal pose estimation with *top-down* and *bottom-up* training strategies
* State-of-the-art pretrained and customizable neural network architectures that deliver *accurate predictions* with *very few* labels
* Fast training: 15 to 60 mins on a single GPU for a typical dataset
* Fast inference: up to 600+ FPS for batch, <10ms latency for realtime
* Support for remote training/inference workflow (for using SLEAP without GPUs)
* Flexible developer API for building integrated apps and customization
Get some SLEAP
--------------
SLEAP is installed as a Python package. We strongly recommend using `Miniconda <https://https://docs.conda.io/en/latest/miniconda.html>`_ to install SLEAP in its own environment.
You can find the latest version of SLEAP in the `Releases <https://github.com/talmolab/sleap/releases>`_ page.
Quick install
^^^^^^^^^^^^^
`conda` **(Windows/Linux/GPU)**:
.. code-block:: bash
conda create -y -n sleap -c conda-forge -c nvidia -c sleap -c anaconda sleap
`pip` **(any OS except Apple silicon)**:
.. code-block:: bash
pip install sleap[pypi]
See the docs for `full installation instructions <https://sleap.ai/installation.html>`_.
Learn to SLEAP
--------------
- **Learn step-by-step**: `Tutorial <https://sleap.ai/tutorials/tutorial.html>`_
- **Learn more advanced usage**: `Guides <https://sleap.ai/guides/>`__ and `Notebooks <https://sleap.ai/notebooks/>`__
- **Learn by watching**: `MIT CBMM Tutorial <https://cbmm.mit.edu/video/decoding-animal-behavior-through-pose-tracking>`_
- **Learn by reading**: `Paper (Pereira et al., Nature Methods, 2022) <https://www.nature.com/articles/s41592-022-01426-1>`__ and `Review on behavioral quantification (Pereira et al., Nature Neuroscience, 2020) <https://rdcu.be/caH3H>`_
- **Learn from others**: `Discussions on Github <https://github.com/talmolab/sleap/discussions>`_
References
-----------
SLEAP is the successor to the single-animal pose estimation software `LEAP <https://github.com/talmo/leap>`_ (`Pereira et al., Nature Methods, 2019 <https://www.nature.com/articles/s41592-018-0234-5>`_).
If you use SLEAP in your research, please cite:
T.D. Pereira, N. Tabris, A. Matsliah, D. M. Turner, J. Li, S. Ravindranath, E. S. Papadoyannis, E. Normand, D. S. Deutsch, Z. Y. Wang, G. C. McKenzie-Smith, C. C. Mitelut, M. D. Castro, J. D’Uva, M. Kislin, D. H. Sanes, S. D. Kocher, S. S-H, A. L. Falkner, J. W. Shaevitz, and M. Murthy. `Sleap: A deep learning system for multi-animal pose tracking <https://www.nature.com/articles/s41592-022-01426-1>`__. *Nature Methods*, 19(4), 2022
**BibTeX:**
.. code-block::
@ARTICLE{Pereira2022sleap,
title={SLEAP: A deep learning system for multi-animal pose tracking},
author={Pereira, Talmo D and
Tabris, Nathaniel and
Matsliah, Arie and
Turner, David M and
Li, Junyu and
Ravindranath, Shruthi and
Papadoyannis, Eleni S and
Normand, Edna and
Deutsch, David S and
Wang, Z. Yan and
McKenzie-Smith, Grace C and
Mitelut, Catalin C and
Castro, Marielisa Diez and
D'Uva, John and
Kislin, Mikhail and
Sanes, Dan H and
Kocher, Sarah D and
Samuel S-H and
Falkner, Annegret L and
Shaevitz, Joshua W and
Murthy, Mala},
journal={Nature Methods},
volume={19},
number={4},
year={2022},
publisher={Nature Publishing Group}
}
}
Contact
-------
Follow `@talmop <https://twitter.com/talmop>`_ on Twitter for news and updates!
**Technical issue with the software?**
1. Check the `Help page <https://sleap.ai/help.html>`_.
2. Ask the community via `discussions on Github <https://github.com/talmolab/sleap/discussions>`_.
3. Search the `issues on GitHub <https://github.com/talmolab/sleap/issues>`_ or open a new one.
**General inquiries?**
Reach out to `talmo@salk.edu`.
.. _Contributors:
Contributors
------------
* **Talmo Pereira**, Salk Institute for Biological Studies
* **Liezl Maree**, Salk Institute for Biological Studies
* **Arlo Sheridan**, Salk Institute for Biological Studies
* **Arie Matsliah**, Princeton Neuroscience Institute, Princeton University
* **Nat Tabris**, Princeton Neuroscience Institute, Princeton University
* **David Turner**, Research Computing and Princeton Neuroscience Institute, Princeton University
* **Joshua Shaevitz**, Physics and Lewis-Sigler Institute, Princeton University
* **Mala Murthy**, Princeton Neuroscience Institute, Princeton University
SLEAP was created in the `Murthy <https://murthylab.princeton.edu>`_ and `Shaevitz <https://shaevitzlab.princeton.edu>`_ labs at the `Princeton Neuroscience Institute <https://pni.princeton.edu>`_ at Princeton University.
SLEAP is currently being developed and maintained in the `Talmo Lab <https://talmolab.org>`_ at the `Salk Institute for Biological Studies <https://salk.edu>`_, in collaboration with the Murthy and Shaevitz labs at Princeton University.
This work was made possible through our funding sources, including:
* NIH BRAIN Initiative R01 NS104899
* Princeton Innovation Accelerator Fund
License
-------
SLEAP is released under a `Clear BSD License <https://raw.githubusercontent.com/talmolab/sleap/main/LICENSE>`_ and is intended for research/academic use only. For commercial use, please contact: Laurie Tzodikov (Assistant Director, Office of Technology Licensing), Princeton University, 609-258-7256.
.. end-inclusion-marker-do-not-remove
Links
------
* `Documentation Homepage <https://sleap.ai>`_
* `Overview <https://sleap.ai/overview.html>`_
* `Installation <https://sleap.ai/installation.html>`_
* `Tutorial <https://sleap.ai/tutorials/tutorial.html>`_
* `Guides <https://sleap.ai/guides/index.html>`_
* `Notebooks <https://sleap.ai/notebooks/index.html>`_
* `Developer API <https://sleap.ai/api.html>`_
* `Help <https://sleap.ai/help.html>`_
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"description": "|CI| |Coverage| |Documentation| |Downloads| |Conda Downloads| |Stable version| |Latest version|\n\n.. |CI| image:: \n https://github.com/talmolab/sleap/workflows/CI/badge.svg?event=push&branch=develop\n :target: https://github.com/talmolab/sleap/actions?query=workflow:CI\n :alt: Continuous integration status\n\n.. |Coverage| image::\n https://codecov.io/gh/talmolab/sleap/branch/develop/graph/badge.svg?token=oBmTlGIQRn\n :target: https://codecov.io/gh/talmolab/sleap\n :alt: Coverage\n\n.. |Documentation| image:: \n https://img.shields.io/badge/Documentation-sleap.ai-lightgrey\n :target: https://sleap.ai\n :alt: Documentation\n \n.. |Downloads| image::\n https://static.pepy.tech/personalized-badge/sleap?period=total&units=international_system&left_color=grey&right_color=brightgreen&left_text=PyPI%20Downloads\n :target: https://pepy.tech/project/sleap\n :alt: Downloads\n \n.. |Conda Downloads| image:: https://img.shields.io/conda/dn/sleap/sleap?label=Conda%20Downloads\n :target: https://anaconda.org/sleap/sleap\n :alt: Conda Downloads\n\n.. |Stable version| image:: https://img.shields.io/github/v/release/talmolab/sleap?label=stable\n :target: https://github.com/talmolab/sleap/releases/\n :alt: Stable version\n\n.. |Latest version| image:: https://img.shields.io/github/v/release/talmolab/sleap?include_prereleases&label=latest\n :target: https://github.com/talmolab/sleap/releases/\n :alt: Latest version\n\n\n.. start-inclusion-marker-do-not-remove\n\n\nSocial LEAP Estimates Animal Poses (SLEAP)\n==========================================\n\n.. image:: https://sleap.ai/docs/_static/sleap_movie.gif\n :width: 600px\n\n**SLEAP** is an open source deep-learning based framework for multi-animal pose tracking `(Pereira et al., Nature Methods, 2022) <https://www.nature.com/articles/s41592-022-01426-1>`__. It can be used to track any type or number of animals and includes an advanced labeling/training GUI for active learning and proofreading.\n\n\nFeatures\n--------\n* Easy, one-line installation with support for all OSes\n* Purpose-built GUI and human-in-the-loop workflow for rapidly labeling large datasets\n* Single- and multi-animal pose estimation with *top-down* and *bottom-up* training strategies\n* State-of-the-art pretrained and customizable neural network architectures that deliver *accurate predictions* with *very few* labels\n* Fast training: 15 to 60 mins on a single GPU for a typical dataset\n* Fast inference: up to 600+ FPS for batch, <10ms latency for realtime\n* Support for remote training/inference workflow (for using SLEAP without GPUs)\n* Flexible developer API for building integrated apps and customization\n\n\nGet some SLEAP\n--------------\nSLEAP is installed as a Python package. We strongly recommend using `Miniconda <https://https://docs.conda.io/en/latest/miniconda.html>`_ to install SLEAP in its own environment.\n\nYou can find the latest version of SLEAP in the `Releases <https://github.com/talmolab/sleap/releases>`_ page.\n\nQuick install\n^^^^^^^^^^^^^\n`conda` **(Windows/Linux/GPU)**:\n\n.. code-block:: bash\n\n conda create -y -n sleap -c conda-forge -c nvidia -c sleap -c anaconda sleap\n\n`pip` **(any OS except Apple silicon)**:\n\n.. code-block:: bash\n\n pip install sleap[pypi]\n\n\nSee the docs for `full installation instructions <https://sleap.ai/installation.html>`_.\n\nLearn to SLEAP\n--------------\n- **Learn step-by-step**: `Tutorial <https://sleap.ai/tutorials/tutorial.html>`_\n- **Learn more advanced usage**: `Guides <https://sleap.ai/guides/>`__ and `Notebooks <https://sleap.ai/notebooks/>`__\n- **Learn by watching**: `MIT CBMM Tutorial <https://cbmm.mit.edu/video/decoding-animal-behavior-through-pose-tracking>`_\n- **Learn by reading**: `Paper (Pereira et al., Nature Methods, 2022) <https://www.nature.com/articles/s41592-022-01426-1>`__ and `Review on behavioral quantification (Pereira et al., Nature Neuroscience, 2020) <https://rdcu.be/caH3H>`_\n- **Learn from others**: `Discussions on Github <https://github.com/talmolab/sleap/discussions>`_\n\n\nReferences\n-----------\nSLEAP is the successor to the single-animal pose estimation software `LEAP <https://github.com/talmo/leap>`_ (`Pereira et al., Nature Methods, 2019 <https://www.nature.com/articles/s41592-018-0234-5>`_).\n\nIf you use SLEAP in your research, please cite:\n\n T.D. Pereira, N. Tabris, A. Matsliah, D. M. Turner, J. Li, S. Ravindranath, E. S. Papadoyannis, E. Normand, D. S. Deutsch, Z. Y. Wang, G. C. McKenzie-Smith, C. C. Mitelut, M. D. Castro, J. D\u2019Uva, M. Kislin, D. H. Sanes, S. D. Kocher, S. S-H, A. L. Falkner, J. W. Shaevitz, and M. Murthy. `Sleap: A deep learning system for multi-animal pose tracking <https://www.nature.com/articles/s41592-022-01426-1>`__. *Nature Methods*, 19(4), 2022\n\n\n**BibTeX:**\n\n.. code-block::\n\n @ARTICLE{Pereira2022sleap,\n title={SLEAP: A deep learning system for multi-animal pose tracking},\n author={Pereira, Talmo D and \n Tabris, Nathaniel and\n Matsliah, Arie and\n Turner, David M and\n Li, Junyu and\n Ravindranath, Shruthi and\n Papadoyannis, Eleni S and\n Normand, Edna and\n Deutsch, David S and\n Wang, Z. 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